Data augmentation is a traditional and effective way of increasing the number of training samples.
[1]} and [2]} proposed the used of the traditional data augmentation technique to construct pretext tasks for unsupervised few-shot learning. [3]} and [4]} leveraged the general idea of data augmentation, they designed a hallucination model to generate the augmented version of the image with different choices for the model's input, i.e., an image and a noise  [3]} or the concatenation of multiple features [4]}.  [7]}, [8]}, [9]} tried to augment feature representations by leveraging intra-class variance. These methods learn to augment from the original samples or their feature representation while we try to estimate the class-level distribution and thus can eliminate the inductive bias from a single sample and provide more diverse generations from the calibrated distribution.
